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Perspectives in Plant Ecology, Evolution and Systematics 12 (2010) 57–65

Contents lists available at ScienceDirect

Perspectives in Plant Ecology, Evolution and Systematics

1433-83

doi:10.1

� Corr

E-m

wild@ib

journal homepage: www.elsevier.de/ppees

Research article

Recording effort biases the species richness cited in plant distribution atlases

Petr Petrık a,�, Jan Pergl a, Jan Wild a,b

a Institute of Botany, Academy of Sciences of the Czech Republic, 25243 Pruhonice, Czech Republicb Department of Applied Geoinformatics and Spatial Planning, Czech University of Life Sciences, 16000 Praha 6, Czech Republic

a r t i c l e i n f o

Article history:

Received 30 May 2006

Received in revised form

28 May 2009

Accepted 24 June 2009

Keywords:

Biodiversity assessment

Botanical mapping

Latitudinal gradient

Scale

Species–area relationship

19/$ - see front matter & 2009 Rubel Founda

016/j.ppees.2009.06.004

esponding author.

ail addresses: petrik@ibot.cas.cz (P. Petrık),

iot.cas.cz (J. Wild).

a b s t r a c t

This paper explores potential biases due to recording effort in the species richness (number of vascular

plant species) recorded in grid-mapping projects. In this study, we review 80 regional and national grid

Central European Basic Area (CEBA) mapping projects on the vascular plant flora of Central Europe.

The measures of recording effort used were the duration of the mapping project, resolution of the

mapping grid and number of botanists involved. Furthermore, several environmental and geographic

factors associated with the variation in species richness were used as covariables: the number of

phytosociological units in the Map of Potential Natural Vegetation of Europe, altitudinal range, annual

precipitation, mean January and June temperatures and geographical location of the study areas. The

effects of individual factors on species richness were compared by multiple regression and hierarchical

partitioning. Both methods indicated a bias in observed species richness due to recording effort.

Multiple regression indicated a significant role of duration of study, and hierarchical partitioning

revealed significant effects of duration, number of botanists and used resolution. Of the variation in the

total number of species recorded, 8% was attributed to the duration of mapping, 9% to the used

resolution, and 7% to the number of botanists involved in mapping. However, this bias was scale-

dependent. Although the sampling effect can be neglected on a broad scale, on a finer scale a significant

amount of the variation in plant species richness can be ascribed to recording effort. This indicates the

need for a standard approach in mapping and analysing patterns of species richness.

& 2009 Rubel Foundation, ETH Zurich. Published by Elsevier GmbH. All rights reserved.

Introduction

Species richness is one of the most common measures ofbiodiversity used by researchers, nature conservation practi-tioners and decision makers. Species richness obtained fromlong-term vegetation and floristic surveys is used as an ecologicalcurrency or for studying macroecological patterns and processesat different scales (Chapman, 2005; Orme et al., 2005). However,like distributional range and species abundance, species richnessis a scale-dependent measure (Palmer and White, 1994; Kunin,1998; Hartley and Kunin, 2003; Storch et al., 2008; Dengler,2009b). To overcome the scale-dependency, species richness datahave been combined with, e.g., species traits (Kuhn et al., 2004b,2006; Schmidtlein, 2004; Knapp et al., 2008), which are thoughtto be scale-independent and relatively resistant to sampling biasor are integrated over different scales (Schamin�ee and Hennekens,2001; Petrık and Bruelheide, 2006). As studies on species–arearelationships indicate that patterns in species richness aregenerally predictable using scaling approaches (Sizling and Storch,

tion, ETH Zurich. Published by Els

pergl@ibiot.cas.cz (J. Pergl),

2004; Palmer, 2006), it should be possible to assess the floristicdiversity of a region independently of scale (Malyshev, 1991), byusing a standard area and species–area relationship (see Arrhe-nius, 1921). Furthermore, studies based on floristic data (e.g.,Palmer, 1994; Heikkinen and Birks, 1996; Moser et al., 2005; seealso review of grid-based data by Petrık and Wild, 2006) clearlyshow that variation in species richness usually reflects thevariation in environmental factors. Species richness may thus bepredicted by scale and environmental factors, which are widelyutilized in predictive models of species richness (e.g., Austin et al.,1996; Ferrier, 2002; Palmer et al., 2002).

However, patterns in species richness are not only affectedby environmental factors. Several factors, mainly linked to theprocess of collating (recording effort) and processing thedata, affect the number of species recorded and may biasthe species richness pattern (see Table 1 for examples). Ingeneral, two main sources of data are used for large andmedium-scale studies of species richness: (1) more-or-lessselective collections of species and their locations within anarea (e.g., herbarium specimens) and (2) more systematicallycollected grid-based data (various levels of detail and scale).The different types of data lead to different types of bias.Data based on museum collections (Applequist et al., 2006;Guralnick and Van Cleve, 2005; Graham and Hijmans, 2006) seem

evier GmbH. All rights reserved.

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Table 1Variables that influenced the species richness recorded in the botanical studies cited.

Variable Effect Reference

Mapping scheme and data sources Duration of study +/0 Rich and Smith (1996)

Number of botanists +/0 Mahecha and Schmidtlein (2007)

Number of herbarium specimens, etc. +/? Loiselle et al. (2008)

Taxonomic concept Status of species (aliens) +/? Pysek (2003), Pysek et al. (2004)

Lumping and splitting taxa –/+ Gaston (1996)

Scale Resolution size + Graham and Hijmans (2006)

Extent of area mapped ++ Gurevitch et al. (2002)

Land-cover Area of forest cover +/� Myklestad (1993)

Land-use Size of urbanised area +/� Deutschewitz et al. (2003), Kuhn et al. (2004a)

Management +/� Spiegelberger et al. (2006)

Geo-diversity Altitudinal range + Petrık and Wild (2006)

Climate Mean temperature and precipitation +/� Kuhn et al. (2003), Pysek et al. (2002a)

Geography Latitude �/+ Finnie et al. (2007)

+, positive effect; ++, very positive effect; � , negative effect; +/� , either positive or negative effect; ?, uncertain effect or scale-dependent; 0, no effect.

P. Petrık et al. / Perspectives in Plant Ecology, Evolution and Systematics 12 (2010) 57–6558

to be highly affected by false positives records (i.e., the speciesis currently absent but declared to be present), whereasin grid-based data, false negatives (present, but declared to beabsent) are common. However, grid-based data minimize thepotential bias due to differences in sampling effort and preferencefor particular sites. The different taxonomic concepts andmapping schemes associated with geographical and politicalboundaries also significantly affect recorded biodiversity(Mahecha and Schmidtlein, 2007).

To allow valid meta-analyses, it is important to identifypotential sources of bias. There have been attempts to asses orcorrect variation in recording effort, for example, by quoting timespent and habitat types surveyed during field work or usingspecies accumulation curves (Prendergast et al., 1993; Rich andWoodruff, 1992; Hortal and Lobo, 2005; Rich and Karran, 2006).Many approaches have been applied in the analysis of dataderived from museum collections (e.g., by using background dataindicating sampling intensity or predictive modeling; see Ponderet al., 2001; Royle et al., 2007; Schulman et al., 2007; Grytnesand Romdal, 2008). The ability to correct for potential biasesis limited by lack of information on the sampling methods used,and taxonomic or geographic accuracy (Hortal et al., 2006).This information is rarely available for mapping projects or cannotbe quantified.

An outstanding opportunity to identify and asses any potentialbias is provided by the European grid-based mapping projectsas these projects cover a large area, which is relatively homo-genous, of similar history, and shares a common species pool andconsistent methodology. These species distributions are usuallyprocessed and published in the form of grid atlases (for examplesof their use, see Scheuerer and Schonfelder, 2000; Haeupler, 2005)or extensive on-line data sets, which are available for Europeand elsewhere (e.g., Floraweb; Worldmap; see Gaston, 2003).To date, about one-fifth of the European flora has been mapped forthe Atlas Florae Europaeae (Finnie et al., 2007) based on a 50 km2

grid. Since the early 1960s, under the Mapping the Flora of

Central Europe programme, many projects in Austria, the CzechRepublic, Germany, Hungary, Northern Italy and Slovenia haveused the CEBA (Central European Basic Area) system (Niklfeld,1999). This system is based on a grid template divided intosquares of 10� 6 arc minutes and is applied at a range of spatialscales.

The aims of this paper are to review the current CentralEuropean grid-based mapping projects using the CEBA system and

analyse these data for potential bias in the number of vascularplant taxa recorded due to variation in recording effort by adjustingfor the effect of environmental and geographical factors.

Materials and methods

Sources of data

We used published atlases, reports on field work andunpublished data as sources. To facilitate comparison, onlyprojects based on the CEBA grid-mapping system were consid-ered. In total, 80 grid atlases and mapping projects were found(Table 2), 53 of which were complete or in the final stages ofelaboration and suitable for analysis. To avoid nesting, seven largeprojects were not included in the statistical analysis due to theirpartial overlap with smaller projects.

For each mapping project the total number of taxa recordedwas used as an estimate of species richness. In addition, the totalnumber of records (one record per taxon and grid-cell) wasdetermined for each project, but not used in further analyses,because it was strongly correlated with number of taxa. The totalnumber of taxa was determined at the species level; taxa recordedat the subspecies and variety level were grouped into highertaxonomic groups. For each mapping projects we determined thefollowing explanatory variables, which we termed atlas character-

istics: (1) duration of the field work in years, (2) total size ofmapped area in km2 (referred to as spatial extent), (3) numberof botanists working on the project, and (4) size of the grid-cells inkm2 used in the atlas (resolution). In order to characterize thediversity of environmental conditions in the regions studied, thefollowing environmental characteristics were further determined:(1) the number of phytosociological units of the Map of PotentialNatural Vegetation of Europe (PNV) in each region (digital versionof Bohn et al., 2003, derived from a map scale of 1:2.5 million) as aunified concept of vegetation across the countries surveyed, (2)altitudinal range (in metres, derived from USGS GTOPO30 digitalterrain model), (3) annual precipitation (in millimetres) and(4) mean January and June temperatures (in degrees Celsius),obtained from WORLDCLIM (Hijmans et al., 2005). Latitude andlongitude of the centroid (in arc degrees) of each study area wereused to characterise the geographical position and referred to asgeographical characteristics. If some of these characteristics werecited in the source, the original data rather than those obtained

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Table 2List of Central European projects that use grid mapping and conform with the CEBA grid system.

Reference and CEBA grid used Extent (km2) Duration (yr) No. records No. taxa No. of botanists

1 CEBAHaeupler and Schonfelder 1988 (D) 357,241 712 1,300,000 2490 1300

Seybold 1977 (D) 22,876 10 97,000 1486 104

Poldini 1991 (I) 7594 720 54,245 2780 45

Poldini 2002 (I)* 7594 28 74,738 3388 35

1 Quadrant (1/4 CEBA)Benkert et al. 1996 (D) 109,176 27 2,200,000 1998 71000

Niklfeld H. and Schratt-Ehrendorfer L. (A) 87,281 38 1,260,000 3300 7400

Schonfelder and Bresinsky 1990 (D) 74,746 16 1,000,000 2496 400

Kir �aly G. et al. (H)* 93,112 15 600,000 2200 120

Haeupler et al. 2003 (D) 34,325 8 7600,000 2136 197+501

Sebald et al. 1992–1998 (D)* 38,603 727 406,000 2075 272

Selvaggi A. et al. (Piemonte region, I) 26,600 6 64,706 3585 40–45

Gironi F., Lenna P., Rossi G., Galasso G., Sartori F. (Lombardia, I) 25,802 5 41,402 3200 50

Haeupler 1976 (D)* 15,532 5 261,888 1704 170

Jogan et al. 2001 (SLO)* 220,684 10 237,199 3192 735

Gatterer et al. 2003 (D) 11,372 19 7250,000? 2640 82+100

Hartl et al. 1992 (A)* 10,607 26 174,000 2588 115

Mergenthaler 1982 (D)* 9476 10 139,214 1554 34

Wittmann et al. 1987 (A)* 7228 20 115,000 1876 73

Niklfeld H. et al. 2006 (Prov. Bozen/Bolzano-South Tyrol, I)* 8911 33 135,892 2578 80

Prosser F. and Festi F. (Prov. Trento, I)* 7515 14 169,000 72500 720

Lang and Wolff 1993 (D)* 6683 22 128,600 2328 151

Pignatti E. and Pignatti S. (Dolomiti Mts., I) 6118 35 105,000 2200 ?

Hand and Bujnoh 1991 (D) 5952 710 ? ? 60

Martini F., Bona E., Fenaroli F., Tagliaferri F. (Prov. Brescia, I)* 5852 13 120,813 72700 17

Herdam 1993 (D)* 4755 8 772,000 836 332

Argenti C. and Lasen C. (Prov. Belluno, I)* 3990 30 754,000 72200 2

Haffner et al. 1979 (D)* 2545 10 55,000 1352 5

Bovio M. and Poggio L. (Valle d’Aosta, I) 3890 6 31,110 71800 75

Prosser F. and Festi F. (Prov. Verona, I)* 3658 4 49,151 72000 710

Perico G., Federici G., Bona E., Martini F. al. (Prov. Bergamo, I)* 3491 8 81,925 2600 14

Scortegagna S. (Prov. Vicenza, I) 3292 6 760,000 72200 ?

Prosser F. and Festi F. (Prov. Mantova, I) – provisional 3259 2 720,000 71500 75

Giordana F. and Bonali F. (Prov. Cremona, I)* 2394 9 32,355 1563 34

Hiemeyer and Radmuller 1978 (D)* 1929 12 20,000 1177 15

Faltys 1990b (CZ)* 1929 9 30,000 1200 6

Faltys 1990a (CZ)* 1463 11 28,500 1421 20

Kleih M. (Prov. Varese, I) 1363 10 7386 1087 6

Dull and Kutzelnigg 1987 (D)* 1330 17 17,000 1800 725

Galunder et al. 1990 (D) 1297 ? 18,344 936 ?

Muller 1991 (D) 798 8 ? 879 1

Bot. Arbeitsgem. Linz 1996 (Dachstein area, A)* 732 29 12,000 1200 40

Costalonga S. and Pavan R. (Col Nudo,Cavallo & Cansiglio Mts., I) 698 15 11,406 1673 4

Costalonga S. and Pavan R. (Livenza River Basin, I) 632 10 8638 1524 2

Costalonga S. and Pavan R. (Plain territory around Pordenone, I) 432 8 74000 955 2

Kutzelnigg and Dull 1989 (A)* 432 16 5009 1042 10

1/16 CEBAHardtke and Ihl 2000 (D)* 18,201 5 976,200 2092 250

Korsch et al. 2002 (D) 18,055 11 1,800,000? 1930 126+307

Lenski 1990 (D) 2660 13 ? 880 18

Becker et al. 1996 (D) 2319 13 116,906 1336 7

Nitsche et al. 1990 (D) 2170 3 ? 1324 76

Jongepier J.W., Jongepierov �a I. et al. (Bıl�e Karpaty Mts., CZ) 1056 4 430,000? 1500 21

yProch�azka F., Stech M. et al. (Sumava Mts., CZ) 873 13 7350,000? 1500 750

Faltys 1986 (CZ) 831 ? ? 978 ?

Ziebell 1997 (D) 698 15 ? 869 1

Klein and Klein 1985 (D) 532 ? ? 998 ?

Poldini L. et al. (Prov. Trieste, I)* 233 35 45,820 2286 10

Festi and Prosser 2000 (I)* 382 7 720,000 1474 3

Stieglitz 1987 (D)* 316 12 720,000 1143 43

1/36 CEBARaabe 1987 (D)* 18,871 27 723,484 1174 75

Tischler 1994 (A)* 148 2 12,795 768 1

Weninger 1995 (A)* 92 2 5218 751 1

Prewein 1996 (A)* 78 3 77000 836 1

1/64 CEBAMeinunger 1992 (D) 13,832 30 ? 71217 713

Meierott and Subal 2005 (D)* 2993 25 660,000 2500 4

Hartmann et al. 1995 (D)* 1342 720 384,451 1770 43

Scheller 1989 (D)* 813 7 114,146 985 1

Klotz J. (Regensburg, D) 798 8 400,000 2000 1

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Table 2 (continued )

Reference and CEBA grid used Extent (km2) Duration (yr) No. records No. taxa No. of botanists

Hartel H., Bauer P. et al. (Labsk�e pıskovce Mts., CZ)* 443 13 770,000 7800 720

Lepsı M., Lepsı L. Boublık K. (Novohradsk�e hory Mts., CZ) 312 3 17,000 71000 3

1/100 CEBAKolbek et al. 1999 (CZ)* 726 18 151,100 1533 46

Danihelka J. et al. (P�alava, CZ)* 355 13 136,000 1300 3

Grulich 1997 (CZ)* 229 5 50,000 1273 1

Vicherek et al. 2000 (CZ)* 76 3 23,000 873 3

1/256 CEBA

Petrık P. (Jestedsky hrbet, CZ)* 111 6 48,840 1079 1

1/324 CEBAMann 1997 (A)* 33 4 17,349 942 1

Martini 2005 (I)* 20 6 13,148 840 10

Gutermann W. and Niklfeld H. (Grobfragrant, A) 20 16 3849 746 6

1/1024 CEBAKresken 2004 (D)* 28 7 24,631 666 1

1/1440 CEBAKraml 2001 (A)* 95 12 93,269 872 1

Martini F. (City area of Trieste, I)* 26 10 48,370 1025 13

Only atlas characteristics are listed. Respective countries of the projects are shown in brackets, for the completed projects see reference list; on-going projects are in italics;

asterisk indicates studies included in analyses; Prov., Italian province; ?, no information or uncertainty. Full references for mapping projects are given in Appendix A.

P. Petrık et al. / Perspectives in Plant Ecology, Evolution and Systematics 12 (2010) 57–6560

from maps were used. The environmental characteristics werederived from a standard overlay or zonal functions in the case ofvector or raster data, respectively, using the GIS software packageArcGIS 9.2 (ESRI, 2006).

Statistical analysis

To define how explanatory variables such as atlas character-istics, environment and geography determine the number ofrecorded taxa in atlases, multiple regression followed by hier-archical partitioning was used. As spatial autocorrelation mayconfound the observed pattern (e.g., Kuhn, 2007), our data weretested for spatial correlation using Moran’s I statistics, as providedby the Passage software (Rosenberg, 2008) with 999 permutationsof two distance classes (50 and 100 km) prior to the analysis.This analysis did not reveal any significant autocorrelation. Thespecies–area relationship (species–sampling curve sensu Dengler,2008) was assessed according to Arrhenius’ (1921) equationlog10 S=c+z* log10 A, where S is species richness and A the spatialextent.

A minimal adequate model (MAM) containing only significantvariables (Crawley, 2002) was developed for species richness.Following the approach used by Lambdon et al. (2008), which isbased on fewer observations and a larger number of explanatoryvariables, we fitted separate models for atlas, environment andgeographical variables including all possible two-way interactions(Quinn and Keough, 2002). Each model was then simplified toobtain MAM for atlas, environment and geographical character-istics. To determine whether this step-wise process omittedany interactions, the MAMs for environmental, geographical andatlas variables were examined for two-way interactions withthe explanatory variables of the other groups, which included allthe explanatory variables but not their interactions. To obtain thefinal MAM, the significant variables were combined into onemodel with all possible two-way interactions and then the modelwas refitted using deletion tests. The potential non-linearity of thegeographical patterns was assessed by using both linear andquadratic terms of latitude and longitude. In order to achieve anormal distribution of errors all explanatory variables were log-transformed and altitudinal range square-root transformed and

checked by plotting standardised residuals against fitted valuesand using normal probability plots (Crawley, 2002).

As step-wise variable selection applied to regression modelsoften fails to discriminate relevant variables from correlatedenvironmental factors (see Meynard and Quinn, 2007), we alsoanalysed the data sets using hierarchical partitioning. Thisapproach allows variables to be retrieved that are likely to havethe greatest importance in determining the variance in data(Quinn and Keough, 2002). Hierarchical partitioning calculates theexplained variance for all possible combinations of independentvariables and gives the proportion of the total variance that can beattributed to independent effects of the individual variables. Thesignificance of the predictors was tested by 999 randomizationsof the explanatory variables using the 95% confidence limit of thez scores (z=[observed value�mean (randomizations)]/sd(rando-mizations) (Mac Nally, 2002). All calculations were made usingthe software R (R Development Core Team, 2008) and hierarchicalpartitioning using R package ‘‘hier.part’’ (Walsh and Mac Nally,2008).

Results

There were large differences in the grid map coverage atthe national level in Central Europe (Table 2). Germany (with 32projects) and Austria (12 projects) were well covered at thenational level. On the other hand, there were countries that werepoorly covered by grid mapping at the national level: Italy with 32small local projects scattered throughout the country and the CzechRepublic (12 projects). In contrast, there were no projects forSlovakia. For those countries for which the national atlases aredelimited administratively or have coastal regions, the marginalgrid-cells were only partially mapped.

The grid-mapping projects varied widely in atlas characteristicsand differed in the way the data were recorded (presence/absenceor quantitatively). Mapping using a quarter of the CEBA basic grid(ca. 33 km2) was most popular, regardless of the size of the regionmapped. The projects lasted longest in Austria (38 years) and theNorthern Italian provinces (mostly over 30 years); while theshortest projects involved only small areas and lasted 2 years(e.g., East Tirol). The average duration of projects was 13 years, the

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P. Petrık et al. / Perspectives in Plant Ecology, Evolution and Systematics 12 (2010) 57–65 61

projects included from 666 to 3192 plant species and the number ofcollaborators varied strongly.

Species richness recorded depended strongly on the spatial extentof the study area: log10 (species richness)=6.233+0.146*log10 (spatialextent) (F1,44=50.43, Po0.001, R2=0.534).

In order to give an overview of the relationships between thespecies richness and atlas, environmental and geographical character-istics, pairwise correlation coefficients are shown in Table 3. Allcharacteristics that reflected the heterogeneity of the environmentin the study areas were positively correlated with the spatial extent.There was a positive correlation between the number of phyto-sociological units of potential natural vegetation (PNV) with all atlascharacteristics, but not with any of the environmental or geographicalvariables describing the heterogeneity of environmental conditions.The analysis indicated further that in areas with a large altitudinalrange large grid-cells were used.

The minimal adequate model obtained by multiple regressionincluded duration of the study, the variable that was used to studythe bias due to recording effort. The model explained 77% of thevariance in species richness and revealed a significant positiveeffect of spatial extent, duration of the study, altitudinal range, andmean January temperature, and a decrease in species richness withincreasing latitude (Table 4). Other variables representing recordingeffort were not significant in this model.

Hierarchical partitioning of the analysed data using the wholeset of explanatory variables gave slightly different results than theregression analysis (Table 4); the full model explained 80.4% of thevariance in species richness. The highest independent contribu-tion was found for spatial extent and PNV (16.8% and 12.9% of thetotal variance, respectively). Although the regression indicatedthat only the duration of the project was significant in explainingthe variance, randomization test of hierarchical partitioningshowed significant contributions of all atlas characteristicsincluding number of botanists and resolution (Table 4). Althoughthe January mean temperature was found to be important inmultiple regression, hierarchical partitioning showed that thisfactor together with mean June temperature and sum ofprecipitation explained only a small proportion of the variancein species richness. In order to analyse the scale-dependency ofbias found in species richness, two separate analyses of hierarch-ical partitioning were carried out, for small (o1400 km2, N=22)and large mapping projects (Appendix B). The analysis of small-scale mapping projects showed significant independent contribu-tion of spatial extent (16.43% of the total variance), duration of thestudy (16.63%) and number of PNV units (11.00%). In large-

Table 3Correlations between variables used in this study (N=46).

Taxa Spatial

extent

Duration No. of

botanists

Resolution Altitud

range

Spatial extent 0.73***

Duration 0.54*** 0.46**

No. of botanists 0.53*** 0.69*** 0.45**

Resolution 0.60*** 0.76*** 0.31* 0.58***

Altitudinal

range

0.56*** 0.48*** n.s. 0.30* 0.60***

No. of PNV units 0.68*** 0.87*** 0.40** 0.61*** 0.68*** 0.62

Precipitation n.s. – – – – 0.47

January

temperature

n.s. – – – – �0.60

July

temperature

n.s. – – – – �0.59

Latitude �0.35* – n.s. n.s. n.s. �0.44

Longitude n.s. – -0.32* n.s. -0.31* n.s.

In the analysis, all variables were log-transformed with the exception of altitudinal

**Po0.01; *Po0.05; n.s., non-significant results; – , not applicable.

scale mapping projects (N=24), the independent contributionsof the atlas characteristics characterizing the recording effort weresmall; number of botanists and duration explained only 2% and 3%of the total variance, while altitudinal range, spatial extent,number of PNV units and latitude explained significant propor-tions of the total variance in species richness.

Discussion

As far as we are aware, the effect of recording effort onestimates of species richness has only been assessed for individualareas (see e.g., Rich, 2006), so this is the first review aimed atdisentangling the bias in estimates of species richness due tovarious components of recording effort, which is based on studiesfrom many regions using the same methods, and after adjustingfor environmental and geographical factors.

This study has demonstrated that compared to other environ-mental and geographical factors, those that represent recordingeffort may significantly influence estimated species richness.Several previous studies have found a significant effect of theduration of mapping on recorded species richness (Rich andWoodruff, 1992; Rich and Smith, 1996; Petrık and Boublık, 2004);however, the majority of studies have analysed changes of speciesrichness over time (e.g., Palmer, 2006; Rich, 2006; Magurran,2008) or used a much finer scale than the present study (Klimeset al., 2001). Our study revealed that the duration of the projectaccounted for 8.3% of the total variation in species richnessconsidering all the projects, and for small mapping projects itaccounted for up to 16.6%, the same contribution as the spatialextent of the study. This analysis of subsets of small and largemapping projects showed that the bias in grid mapping of floraseems to be dependent on spatial scale. Another scale parameter,the resolution (size of grid-cells) of the studies, is known to be animportant factor affecting estimates of species richness, withmore accurate results recorded for smaller grid-cells (Graham andHijmans, 2006). The results of our study are in accordance withthis general pattern as resolution accounted for a relatively large(9.3%) and highly significant proportion of the total variation inestimated species richness.

We found that all of the factors representing recording effort(i.e., number of botanists involved and duration of the study) areassociated with some level of bias in estimates of species richness.However, there are at least two other factors that may play a role.The first one is the use of different taxonomic concepts, namely

inal No. of PNV

units

Precipitation January

temperature

July

temperature

Latitude

***

*** n.s.

*** n.s. n.s.

*** n.s. �0.30* 0.88***

** n.s. �0.59*** n.s. n.s.

n.s. n.s. n.s. n.s. –

range, which was square root transformed. Symbols used in table: ***Po0.001;

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Table 4Variables influencing the number of taxa.

Minimal adequate models Hierarchical partitioning

E SE t P I T

Intercept 8.48 0.88 9.65 o0.001

Atlas characteristics 41.7 50.14Spatial extent (km2) 0.11 0.02 5.02 o0.001 16.82* 17.84

Duration (yr) 0.13 0.05 2.57 0.01 8.28* 10.0

No. of botanists – – – – 7.33* 9.79

Resolution – – – – 9.26* 12.5

Environmental characteristics 30.41 25.42Altitudinal range (m) 0.008 0.004 2.13 0.04 9.05* 10.65

Number of PNV units – – – – 12.90* 15.84

Precipitation (mm) – – – – 1.65 0.71

January temperature (1C) 0.04 0.02 2.69 0.01 3.18 �0.89

July temperature (1C) – – – – 3.61 �0.89

Geographical characteristics 8.3 4.85Latitude �0.05 0.02 �2.74 0.01 6.51* 3.3

Latitude2 – – – –

Longitude – – – – 1.79 1.55

Longitude2 – – – –

Latitude*Longitude2 – – – –

Results of minimal adequate models. Given are parameter estimates (E), standard errors (SE), t-values (t) and significance (P) of the effect of the various variables on the

total number of taxa (F5,40=27.19, Po0.001, R2=0.77). In the analysis, all variables were log-transformed with the exception of altitudinal range, which was square root

transformed. Independent (I) and total (independent and joint) (T) contribution of individual variables from hierarchical partitioning (%) to the total variance, *significant

independent effect as revealed by hierarchical partitioning. The groups of factors are in bold; ‘‘–’’ not selected by models.

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splitting or lumping taxa and subtaxa, which can influenceestimates of species richness (Gaston, 1996; see also Table 1).Second, the occurrence of casual species (i.e., species recordedonly once) can be important if there are many such speciesand their proportion increases with the duration of the study(Rich and Woodruff, 1992; Gaston, 1996). For instance, aliencasuals form 22% of the species inventory in the Czech Republic(Pysek et al., 2002b; see also significant effect of temporarilyoccurring wool aliens on the number of species in floras byPysek, 2005) but this is based on only a few occurrences. Further,it is known that a high sampling activity, especially in theproximity of ‘‘botanical hotspots’’, may have the consequence thatcertain plant species are more likely to be recorded than others(Wohlgemuth, 1993; Hortal et al., 2006; Moerman and Estabrook,2006).

The factor that explained the highest amount of variation inspecies richness was the spatial extent of the mapping projects.The parameter z, describing the slope of the species–area curve isreported to vary between 0.1 and 0.5 depending on the spatialscale (Crawley and Harral, 2001) stressing the need to asses thescale issue (see Dengler, 2008). We found a value of the slope z

(0.14) that was similar to the values known from the nemoralsubzone (0.13–0.14; Malyshev, 1991) or the temperate broadleafand mixed forest biome (0.17; Kier et al., 2005). The species–areacurve thus appears to be similar both for the data based on thefloras (Malyshev, 1991) and the data based on grid-mappingprojects.

The observed relationship between latitude and speciesrichness per site was in accordance with the general pattern of adecrease in species richness with increasing latitude in Europe(Malyshev et al., 1994). Several hypotheses have been proposed toaccount for this latitudinal gradient but there is no generalconsensus (e.g., Willig et al., 2003). Although the latitudinal spanof the data included in our study is only nine arc degrees and thevariable ‘‘latitude’’ is complex (Hawkins and Diniz-Filho, 2004),the latitudinal gradient appears to be associated with energy-supply (climatic variables such as precipitation and meantemperatures) and environmental heterogeneity (altitudinalrange) (Table 4, see Wright et al., 1993). However, model selection

and hierarchical partitioning revealed that the individual climaticvariables had only a weak effect on species richness.

Conclusion

The potential biases due to recording effort, which areaddressed in this paper, and the effect of using differenttaxonomic concepts must be taken into account when sharinglarge data sets (see Parr and Cummings, 2005; Palmer et al., 2008;Storch and Sizling, 2008). Although the general trend derived fromsuch mapping projects may be unaffected, in some comparative ormonitoring studies, these biases may result in false conclusions.This is particularly important in regional studies of area up toca. 1500 km2 or large data sets incorporating data amongdifferent spatial scales e.g., ‘‘ALARM: Assessing LArge scale Risksfor biodiversity with tested Methods’’ (Settele et al., 2005)or DAISIE (DAISIE, 2009; Lambdon et al., 2008). Therefore, it iscrucial to clearly state the methods and standards that areto be used for recording species when starting projects (e.g.,inclusion of spatial and environmental variation as well as acorrection for the amount of time spent recording, see e.g.,Hedenas et al., 2002; Delisle et al., 2003). Such standards arealready applied for habitat mapping at the landscape scale (seeBunce et al., 2008), for re-interpretation of published data (Richand Karran, 2006), or for the quality assessment of speciesdatabases (Hortal et al., 2006). However, suitable standards formonitoring purposes at the habitat scale do not exist (Dengler,2009a).

To sum up, the non-critical use of data may be misleading asthe data are influenced by many unpredictable factors and use ofimproper biodiversity statistics such as obtained by standardiza-tion through dividing by area or number of individuals (Palmeret al., 2008). We may avoid potential biases by using a standardmethod (Fehmi and Bartolome, 2001; Hortal and Lobo, 2005; Elithet al., 2006) such as grid mapping. However, even results based ongrid mapping can be influenced by a sampling effect. For moreeffective floristic sampling we recommend the following (see e.g.,Rich and Woodruff, 1992): (1) train and improve the skills of

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botanists, (2) make botanists visit many areas for a short timerather than one area for a long time, (3) and visit all habitats, (4)standardize recording coverage by specifying the number of hoursor number of visits, and (5) try to achieve adequate seasonalcoverage.

Acknowledgements

We would like to thank Harald Niklfeld for his great help withthe review of grid atlases. We wish to thank Bob Bunce, MilanChytry, Jurgen Dengler, Tatjana Good, Ingolf Kuhn, DiethartMatthies, Petr Pysek, David Storch and two anonymous reviewersfor their critical comments on previous versions of the manu-script. Tony Dixon improved the English language in this manu-script. We are also indebted to all the people listed in Table 2 andMichael Breuer for kindly allowing us to share their researchresults. This research was mainly supported by the BiodiversityResearch Centre (Grant no. LC 06073 MSMT CR) and institutionallong-term research plan no. AV0Z60050516. J.P. was supported byGrant no. IAA600050811 from the Academy of Sciences of theCzech Republic.

Appendix A. References used for Table 2

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Appendix B

Summary of the results of hierarchical partitioning of theeffects of various variables on the number of species recorded in(A) small (o1400 km2) and (B) large (41400 km2) mappingprojects. In the analyses, all variables were log-transformed withthe exception of altitudinal range, which was square roottransformed. The resolution was excluded from the analyses dueto low variance after splitting the whole dataset. Independent (I)and total (independent and joint) (T) contribution of individualvariables from hierarchical partitioning (%) to the total variance, *indicates significant results (being outside the 0.95 confidencerange) after 999 randomizations.

(A) Small mapping projects (N=22); multiple regressionequation for the number of taxa recorded: F11,10=2.573,Po0.074, R2=0.740)

(B) Large mapping projects (N=24); multiple regressionequation for the number of taxa recorded: F11,12=4.257, Po0.01,R2=0.796)

(A) Smallprojects

(B) Largeprojects

I

T I T

Spatial extent (km2)

16.43* 19.44 16.46* 20.20 Duration (yr) 16.63* 17.93 3.11 3.20 No. of botanists 9.18 13.50 2.26 0.32

Altitudinal range

5.38 3.50 11.35* 14.88 Number of PNV units 11.00* 14.80 12.01* 15.93 Precipitation (mm) 1.40 0.28 3.02 3.51 January temperature (1C) 2.67 1.03 5.29 0.26 July temperature (1C) 5.48 0.30 8.48 2.11 Latitude 3.80 0.00 16.49* 19.07 Longitude 2.04 3.22 1.13 0.12

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